Dataset Shift in Machine Learning

Name: Dataset Shift in Machine Learning
Price: 7.35 USD
Availability: InStock
ISBN: 9780262170055

ISBN-10: 0262170051

ISBN-13: 9780262170055

Edition: 2009

Authors: Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence, Joaquin Quinonero-Candela

List price: $45.00

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Description:

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of…

Book details

List price: $45.00
Copyright year: 2009
Publisher: MIT Press
Publication date: 12/12/2008
Binding: Hardcover
Pages: 248
Size: 8.00" wide x 10.00" long x 0.75" tall
Weight: 1.540
Language: English

Joaquin Quinonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.

Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.

Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.

Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.




Introduction to dataset shift



When training and test sets are different: characterizing learning transfer




Projection and projectability




Theoretical views on dataset and covariate shift



Binary classification under sample selection bias




On Bayesian transduction: implications for the covariate shift problem




On the training/test distributions gap: a data representation learning framework




Algorithms for covariate shift



Geometry of covariate shift with applications to active learning




A conditional expectation approach to model selection and active learning under covariate shift




Covariate shift by kernel mean matching




Discriminative learning under covariate shift with a single optimization problem




An adversarial view of covariate shift and a minimax approach




Discussion



Author comments



References


Notation and symbols


Contributors


Index